Chap12. Exp Syst

8/6/2019 Chap12. Exp Syst

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Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. AronsonCopyright 1998, Prentice Hall, Upper Saddle River, NJ

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Chapter 12: Fundamentals of

Expert Systems

12.1 Opening Vignette:

CATS-1 at General Electric

The Problem

General Electric's (GE)Top Locomotive Field Service

Engineer was Nearing Retirement


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Traditional Solution:

Apprenticeship

Good Short-term Solution

BUT GE Wanted A more effective and dependable wayto disseminate expertiseTo prevent valuable knowledge fromretiringTo minimize extensive travel ormoving the locomotives


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The Expert System

SolutionTo MODEL the way a humantroubleshooter works

Months of knowledge acquisition 3 years of prototyping

A novice engineer or technician canperform at an experts level

On a personal computer Installed at every railroad repair shop

served by GE


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12.2 (ES) Introduction

Expert System : from the termknowl edge-based expert system

An Expert System is a system thatemploys human knowledgecaptured in a computer to solveproblems that ordinarily requirehuman expertiseES i m i tate the experts reasoning

processes to solve specific


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12.3 History of

Expert Systems1. Ear l y t o M i d- 1960 s

One attempt: the General-purpose Problem

Solver (GPS)General-purpose Problem Solver (GPS)

A procedure developed by Newell andSimon [1973] from their Logic TheoryMachine -

Attempted to create an "intelligent"computer

Predecessor to ES Not successful, but a good start


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2 . M i d- 1960 s: Special-purpose ES programs

DENDRAL MYCIN

Researchers recognized that the problem-solving mechanism is only a small part of acomplete, intelligent computer system

General problem solvers c a nno t be used to buildhigh performance ES

H uman problem solvers are good only if theyoperate in a very n arr ow d oma i n

Expert systems must be constantly u pdated withnew information

The complexity of problems requires aconsiderable amount of knowl edge ab o u t the

pr ob l em area


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3. M i d 1970 s Several Real Expert Systems Emerge Recognition of the Central Role of

Knowledge AI Scientists Develop

Comprehensive knowledge representationtheoriesGeneral-purpose, decision-making proceduresand inferences

Limited Success Because Knowledge is Too Broad and Diverse Efforts to Solve Fairly General Knowledge-

Based Problems were Premature


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BUTSeveral knowl edge represe n tat i on sworked

Key InsightT he p ow er of a n ES i s der iv ed f r om

the spe c i f i c knowl edge i t p ossesses,no t f r om the part i cu l ar fo rma l i smsa n d i nf ere n c e s c hemes i t emp lo ys


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4 . Ear l y 1980 s

ES Technology Starts to go Commercial X CON X SEL

CATS-1Programming Tools and Shells Appear

EMYCIN E X PERT META-DENDRAL EURISKO

About 1/3 of These Systems Are VerySuccessful and Are Still in Use


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Latest ES Developments

Many tools to expedite theconstruction of ES at a reduced cost

Dissemination of ES in thousands of organizationsExtensive integration of ES with otherCBISIncreased use of expert systems inmany tasksUse of ES technology to expedite IS

construction


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The object-oriented programming

approach in knowledge representationComplex systems with multipleknowledge sources, multiple lines of reasoning, and fuzzy informationUse of multiple knowledge basesImprovements in knowledgeacquisition

Larger storage and faster processingcomputersThe Internet to disseminate softwareand expertise.


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12.4 Basic Concepts of

Expert SystemsExpertiseExpertsTransferring ExpertiseInferencing Rules

Explanation Capability


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ExpertiseExpertise is the extensive, task-specific knowledge acquired fromtraining, reading and experience

Theories about the problem area H ard-and-fast rules and procedures Rules (heuristics) Global strategies Meta-knowledge (knowledge about

knowledge) Facts

Enables experts to be better and fasterthan nonexperts


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IS In Focus 12.2: Some

Facts about ExpertiseExpertise is usually associated with ahigh degree of intelligence, but notalways with the smartest personExpertise is usually associated with avast quantity of knowledgeExperts learn from past successes andmistakesExpert knowledge is well-stored,organized and retrievable quicklyfrom an expert

Experts have excellent recall


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Experts

Degrees or levels of expertiseNonexperts outnumber expertsoften by 100 to 1


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H uman Expert Behaviors

Recognizing and formulating theproblem

Solving the problem quickly andproperlyExplaining the solution

Learning from experienceRestructuring knowledgeBreaking rulesDetermining relevanceDe radin racefull awareness of


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Transferring Expertise

Objective of an expert system To transfer expertise from an expert to a computer

system and Then on to other humans (nonexperts)

Activities Knowledge acquisition Knowledge representation Knowledge inferencing Knowledge transfer to the user

Knowledge is stored in a knowl edge base


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Two Knowledge Types

FactsProcedures (Usually Rules)

Regarding the Problem Domain


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Inferencing

Reasoning (Thinking)The computer is programmed sothat it can make inferencesPerformed by the I nf ere n c e

E n g i n e


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Rules

IF-T H EN-ELSE

Explanation Capability By the justifier, or explanation

subsystem

ES versus Conventional Systems(Table 12.1)


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TABLE 12.1 Comparison of Conventional Systems and Expert Systems

Conventional Systems Expert Systems

Information and its processing are usually combined in

one sequential program

Knowledge base is clearly separated from the processing

(inference) mechanism (i.e., knowledge rules separated fromthe control)

Program does not make mistakes (programmers do) Program may make mistakes

Do not (usually) explain why input data are needed orhow conclusions are drawn

Explanation is a part of most ES

Require a ll input data. May not function properly withmissing data unless planned for

Do no t require all initial facts. Typically can arrive atreasonable conclusions with missing facts

Changes in the program are tedious Changes in the rules are easy to accomplish

The system operates only when it is completed The system can operate with only a few rules (as the firstprototype)

Execution is done on a step-by-step (algorithmic) basis Execution is done by using heuristics and logic

Effective manipulation of large databases Effective manipulation of large knowledge bases

Representation and use of data Representation and use of knowledge

Efficiency is a major goal Effectiveness is the major goal

Easily deal with quantitative data Easily deal with qualitative data

Use numerical data representations Use symbolic knowledge representations

Capture, magnify and distribute access to numeric dataor to information

Capture, magnify and distribute access to judgment andknowledge


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12.5 Structure of

Expert SystemsDevelopment EnvironmentConsultation (Runtime)Environment

(Figure 12.2)


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Three Major ES

ComponentsKnowledge BaseInference EngineUser Interface


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Knowledge Acquisition

SubsystemKnowledge acquisition is theaccumulation, transfer andtransformation of problem-solving expertise from expertsand/or documented knowledge

sources to a computer programfor constructing or expanding theknowledge baseRequires a knowledge engineer


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Knowledge Base

The knowledge base contains the knowledgenecessary for understanding, formulating,and solving problems

Two Basic Knowledge Base Elements Facts Special heuristics, or rules that direct the use of

knowledge

Knowledge is the primary raw material of ES Incorporated knowledge representation


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Inference Engine

The bra i n of the ESThe control structure or the ruleinterpreterProvides a methodology forreasoning


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Inference Engine

Major ElementsInterpreterSchedulerConsistency Enforcer


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User Interface

Language processor for friendly,problem-oriented communicationNLP, or menus and graphics


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Blackboard (Workplace)

Area of working memory to Describe the current problem Record Intermediate results

Records Intermediate H ypothesesand Decisions1. Plan2. Agenda3. Solution


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Explanation Subsystem

(Justifier)Traces responsibility and explains theES behavior by interactively

answering questions Why?H ow?

What?

(Where? When? Who?)Knowledge Refining System

Learning for improving performance


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12.6 The Human Element in

Expert SystemsBuilder and UserExpert and Knowledge engineer.

The Expert H as the special knowledge,

judgment, experience and methodsto give advice and solve problems

Provides knowledge about task

performance


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The Knowledge Engineer

H elps the expert(s) structure theproblem area by interpreting andintegrating human answers toquestions, drawing analogies,posing counterexamples, and

bringing to light conceptualdifficulties

Usually also the System Builder


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The UserPossible Classes of Users

A non-expert client seeking direct advice -the ES acts as a C on s u l ta n t or Ad vi s or

A student who wants to learn - an I n str uc t o r

An ES builder improving or increasing theknowledge base - a P art n er

An expert - a C oll eag u e or Ass i sta n t

The Expert and the KnowledgeEngineer Should Anticipate Users'Needs and Limitations When

Designing ES


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Other Participants

System BuilderTool Builder

VendorsSupport Staff

Network Expert(Figure 12.3)


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ES Development

Construction of the knowledge baseKnowledge separated into

D e c l arat iv e (factual) knowledge and P r o c ed u ra l knowledge

Construction (or acquisition) of aninference engine, a blackboard, anexplanation facility, and any othersoftwareDetermine appropriate knowledge

representations


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Domain ExpertKnowledge Engineer and(Possibly) Information System

Analysts and Programmers

Participants


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ES Shell

Includes All Generic Componentsof an ESN o Knowledge

EMYCIN from MYCIN RESOLVER (was E X SYS)


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Consultation

Deploy ES to Users (TypicallyNovices)ES Must be V ery Easy to Use

ES Improvement By Rapid Prototyping


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12.8 An Expert System

at WorkSee text or do a demo in Resolver

(Exsys)


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12.9 Problem Areas Addressedby Expert Systems

Generic Categories of Expert Systems (Table12.2)

Interpretation systems Prediction systems Diagnostic systems Design systems Planning systems Monitoring systems

Debugging systems Repair systems Instruction systems Control systems

Example in H uman Resource Management(Table 12.3)


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TABLE 12.2 Generic Categories of Expert SystemsCategory Problem Addressed

Interpretation Inferring situation descriptions from observations

Prediction Inferring likely consequences of given situations

Diagnosis Inferring system malfunctions from observations

Design Configuring objects under constraints

Planning Developing plans to achieve goal(s)

Monitoring Comparing observations to plans, flagging

exceptions

Debugging Prescribing remedies for malfunctions

Repair Executing a plan to administer a prescribed remedy

Instruction Diagnosing, debugging and correcting student

performance

Control Interpreting, predicting, repairing and monitoring

system behaviors


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TABLE

. Exper t st ems i H man es rce anagemen t ategory

Planning y Emp loymentr equ ireme nty Existing emp loymentinv entoryy Strat egic planning

pPlann ingES

Eliminat eany ga ps that may existb etweensupp ly andd emand p

y Recruitmenty Layoff y Overti mey Retireme nty Dismissal

Int erpretation,

Design

y Organizationand process charty Charact eristics of th e joby Requ iredbehaviorsy Emp loyee charact eristics

p J obAna lysis ESFiteachjob into th e total fabric of th eorganization

py Job descri ptiony Job specification

Design, planningprediction

y Emp loyers r equ ireme ntsy Candidat es perfor manc ey Recruiting goalsy Stat eof labor mark ety Stat us of possibl e insid e recruits

pRecru it men t ES

Disting uishb etween qualifications of potential a pplicants andd esirabl eones p

y Methods of r ecruitingy Sources of r ecruitsy Recruiting policies

Planning, d esign,int erpretation,diagnosis

y A pplicationblanky Personality and perfor manc e testy Physical examinationy Selectioncrit eria

pSelec ti onES

Mostlik elymee torganizations standards of perfor manc eandincr ease the proportionof successf ul emp loyees selected

py Selectionratioy A cceptanc e/Re jection

Design, planningmonitoring, control

y Individ ual diff erencey Profita mountof organizationy Contrib utions to mee ting organizational

goals

pCompen sati onES

Meetall th ecrit eria; equ itabl e to emp loyer and emp loyee; inc entiv e providing; andacc eptabl eto th e emp loyee

p

y Methods of paymenty Incentiv e formy Pay l evely Pay str ucturey Rate rang es

Monitoring,int erpretation,instr uction, control

y Quality and quantity or worky Job knowl edgey Personal qualiti esy Perfor manc estandardsy Eval uation policies

pPer f ormanceEva lua ti onES

Eval uate emp loyee perfor manc e to mee twithstandards p

y Salary adj ustmenty Pro motiony Improveme ntof perfor manc ey Layoff y Transf erence

Debugging,

instr uction, control

y Organizations n eedsy K nowl edge, skill, andability n eeds to

perfor mthe joby Emp loyees needs, perfor manc e, andcharact eristics p

Tra iningES

Matchth eorganizations obj ectiv es withth e firms humantal ent, str ucture, climate, andefficiencyp

y Determining training n eedsy Developing training crit eria

y Choosing train ers andtrain eesy Determining training ty pey A ssign ment, plac eme nt, and

orientationfollow- up

Monitoring, d esign,planning

y Stat eof th e econo myy Goals of th ebargaining parti esy Public s enti menty Issues being disc ussedy Labor laws andr egulationy Pr ecedents inbargaining

pLabor - anagemen t Relati on s ES

Negotiat ewithth e uniononth econtract p

y Contracty Locko uty A rbitration

(Source: Bas ed onBy um, D. H. and E. H. Soh, H uman Resource Manag eme nt Expe rt Syst emsTechnology, Expert Systems, May 1994 .


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12.10 Benefits of

Expert SystemsMajor P o te n t i a l ES Benefits

Increased Output and Productivity Decreased Decision Making Time Increased Process(es) and Product Quality Reduced Downtime Capture of Scarce Expertise Flexibility Easier Equipment Operation Elimination of the Need for Expensive

Equipment


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Operation in H azardous Environments

Accessibility to Knowledge and H elp Desks Increased Capabilities of Other Computerized

Systems Integration of Several Experts' Opinions Ability to Work with Incomplete or Uncertain

Information Provide Training Enhancement of Problem Solving and Decision

Making Improved Decision Making Processes Improved Decision Quality Ability to Solve Complex Problems Knowledge Transfer to Remote Locations Enhancement of Other CBIS

(provide intelligent capabilities to large CBIS)


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Lead to

Improved decision makingImproved products and customerservice

A sustainable strategic advantage

Some may even enhance theorganizations image


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12.11 Problems and

Limitations of Expert SystemsKnowledge is not always readily availableExpertise can be hard to extract from

humansEach experts approach may be different, yetcorrectH ard, even for a highly skilled expert, towork under time pressureUsers of expert systems have naturalcognitive limitsES work well only in a narrow domain of knowledge


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Most experts have no independent means tovalidate their conclusionsThe vocabulary of experts is often limitedand highly technicalKnowledge engineers are rare and expensiveLack of trust by end-usersKnowledge transfer is subject to a host of perceptual and judgmental biasesES may not be able to arrive at conclusionsES sometimes produce incorrectrecommendations


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Longevity of Commercial

Expert Systems(Gill [1995])

Only about one-third survived five years

Generally ES Failed Due to ManagerialIssues Lack of system acceptance by users Inability to retain developers Problems in transitioning from development to

maintenance Shifts in organizational priorities

Proper management of ES development anddeployment could resolve most


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A friendly user interface The problem must be important and

difficult enough Need knowledgeable and high quality

system developers with good people skills The impact of ES as a source of end-users

job improvement must be favorable. Enduser attitudes and expectations must beconsidered

Management support must be cultivated.

Need end-user training programsThe organizational environmentshould favor new technology adoption


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For Success

1. Business applications justified bystrategic impact (competitiveadvantage)

2. Well-defined and structuredapplications


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12.13 Types of

Expert SystemsExpert Systems Versus Knowledge-based Systems

Rule-based Expert SystemsFrame-based SystemsH ybrid Systems

Model-based SystemsReady-made (Off-the-Shelf) SystemsReal-time Expert Systems


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12.14 Expert Systems and

the Internet/Intranets/Web1. Use of ES on the Net2. Support ES (and other AI

methods)


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Using ES on the Net

To provide knowledge and advice to largenumbers of usersH elp desks

Knowledge acquisitionSpread of multimedia-based expert systems(Intelimedia systems)

Support ES and other AI technologiesprovide to the Internet/Intranet(Table 12.4)

TABLE 12 4 Artificial Intelligence Contributions to the


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TABLE 12.4 Artificial Intelligence Contributions to theInternet

Technology Application

Intelligent Agents Assist Web browsing

Intelligent Agents Assist in finding information

Intelligent Agents Assist in matching items

Intelligent Agents Filter E-mail

Intelligent Agents

Expert Systems

Access databases, summarize information

Intelligent Agents Improve Internet security

Expert systems Match queries to users with canned answers tofrequently asked questions (FAQ)

W W W Robots (spiders),Intelligent Agents

Conduct information retrieval and discovery, smartsearch engines (metasearch)

Expert Systems Intelligent browsing of qualitative databases

Expert Systems Browse large documents (knowledge decomposition)

Intelligent Agents Monitor data and alert for actions (e.g., looking forwebsite changes), monitor users and usage


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Summary

Expert systems imitate the reasoning processof expertsES predecessor: the General-purposeProblem Solver (GPS).

Failed - ignored the importance of specificknowledge

The power of an ES is derived from itsspecific knowledge

Not from its particular knowledge representationor inference scheme

Expertise is a task-specific knowledgeacquired from training, reading, andexperienceExperts can make fast and good decisions


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Most of the knowledge in organizations ispossessed by a few expertsExpert system technology attempts totransfer knowledge from experts anddocumented sources to the computer andmake it available to nonexpertsExpert systems involve knowledgeprocessing, not data processingInference engine provides ES reasoningcapabilityThe knowledge in ES is separated from the

inferencingExpert systems provide limited explanationcapabilities

A distinction is made between a development

environment (building an ES) and a


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The major components of an ES are theknowledge acquisition subsystem, knowledgebase, inference engine, blackboard, userinterface and explanation subsystemThe knowledge engineer captures theknowledge from the expert and programs itinto the computer

Although the major user of the ES is a

nonexpert, there may be other users (such asstudents, ES builders, experts)Knowledge can be declarative (facts) orprocedural

Expert systems are improved in an iterative


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The ten generic categories of ES:

interpretation, prediction, diagnosis, design,planning, monitoring, debugging, repair,instruction and controlExpert systems can provide many benefits

Most ES failures are due to non-technicalproblems (managerial support and end usertraining)

Although there are several limitations tousing expert systems, some will disappearwith improved technology


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CASE APPLICATION 12.1: GateAssignment Display System

(GADS)Case Questions

1. Why is the gate assignment task so

complex?2. Why is GADS considered a real-time ES?3. What are the major benefits of the ES over

the manual system? (Prepare a detailedlist.)

4. What measures were taken to increase thereliability of the system and why werethey needed?


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CASE APPLICATION 12.2:Expert System in Building

Construction (EXSOFS)


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CASE APPLICATION W12.1:DustPro--Environmental

Control in Mines

APPENDIX 12 A: Expe r t Sys t ems C i ted in Chap te r


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APPENDIX 12 -A: Expe r t Sys t ems C i ted i n Chap te r

System Vendor (Developer) Description

DENDRA L S t for

U i e rsit

(S t for

, C A )It i f ers t

e mol ecul r structur e of u

o compou

sfrom m ss sp ectr l

ucl ea r m agne tic r espo n se

a ta . T

es st em us es a sp eci a l a lg orit

m to s st em a tic a ll en um e r a tea ll possi le mol ecul a r structur es; it us es c

emic a l e ! p e rtis eto pru ne t

is list of possi iliti es to a m anageab le siz e ."

n o ledge in DENDRAL is r epr esen ted a s a proc ed ur a lco de .

E U R ISKO S tan for d U n i e rsit

(S tan for d , C A )It l ea r n s new

euristics and new d om a in -sp ecific de fi n itio n sof co n cepts i n a pro b lem d om a in . T

e s st em c an lea r n byd isco e r y in a n um be r of d iff e r en t pro b lem d om a in s,in clu d ing V LS I de si gn . E U R ISKO op e r a tes by gene ra ti ng adev ic e co n fi g ur a tio n , computi ng its i n put /output behav ior,a ss essi ng its fu n ctio na lit y and then eva lu a ti ng it aga in stot he r comp a rab le dev ic es.

# E T A -DENDRA L S tan for d U n ive rsit y

(S tan for d , C A )

It he lps c he mists de te rmi ne the de p enden ce of m a ss

sp ectrom e tric fr ag m en ta tio n on su bstructur a l f ea tur es. I td oes t h is by d isco ve ri ng fr ag m en ta tio n rul es for g ivencl a ss es of mol ecul es. M E T A -DENDRAL first gene r a tes a se tof h igh ly sp ecific rul es t ha t a ccou n t for a si ng lefr ag m en ta tio n proc ess i n a p a rticul a r mol ecul e . T hen it us esthe tr a in ing e ! a mpl es to gene r a liz e the se rul es. Fi na ll y, t hesyst em r ee ! a mi ne s t he rul es to r emo ve r ed u ndan t or Syst em

V end or ( D eve lop e r ) D escriptio n in corr ect rul es.

S T EA M ER U .S . N avy incoop e r a tio n w it h $ olt,$ e r anek , and N ew m an

In c.(C a m b ri dge , M A)

It is an in te lli gen t C A I tha t i n structs N avy w it h p e rso nne l i nthe op e r a tio n and m a in tenan ce of t he propulsio n pl an t for a1078 -c l a ss fri ga te . T he syst em c an mo n itor t he stu den t

e ! ecuti ng the boil e r li gh t-off proc ed ur e for t he pl an t,a ckn ow ledge a ppropri a te stu den t a ctio n s, and corr ectina ppropri a te one s. T he syst em w or k s by tying a simul a tio nof t he propulsio n pl an t to a sop h istic a ted g r a p h ic i n te rf a cepro g r a m t ha t d ispl ay s an im a ted color d iag r a ms of pl an tsu bsyst ems . T he stu den t c an m an ipul a te simul a tedcompo nen ts li ke va lve s, s w itc he s, and pumps, and obse r vethe e ff ects o n pl an t p a r a m e te rs, suc h a s c hange s i n pr essur e ,temp e r a tur e and flo w . S T EA M ER us es an ob ject-ori en tedrepr esen ta tio n sc he m e .


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APPENDIX 12-B:

Classic Expert Systems

MYCINX -CON


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1. MYCIN

To aid physicians in diagnosing meningitisand other bacterial blood infections and toprescribe treatmentTo aid physicians during a critical 24-48-hourperiod after the detection of symptoms, atime when much of the decision making isimprecise

Early diagnosis and treatment can save apatient from brain damage or even death

Stanford Medical School in the 1970s by

Dr. Edward H . Shortliffe


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MYCIN Features

Rule-based knowledgerepresentation

Probabilistic rulesBackward chaining methodExplanationUser-friendly system

2 XCON (Expert VAX


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2. XCON (Expert VAXSystem Configuration and

Mass Customization)Digital Equipment Corp. (DEC)

minicomputer systemconfigurationManually: Complex task, manyerrors, not cost effectiveCost savings estimated at about$15 million / yearLiterature: Over $40 million / year

l l


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APPE NDIX 12-C: Typical Expert Systems Applicat ions

Ma n u f a c tu r i n g/E n g i n eer i n g Product designDesign analysisProcess planning

Assembly mgt.

Process controlDiagnosis & repairSchedulingRosteringSimulationCost estimationConfiguration

Ut i l i t i es & T elec o mm u n i c at i on sConfigurationReal-time monitoring

A larm analysisDiagnosis

Network analysisMarketing analysisMarketing supportBack office operationsSchedulingBilling OperationsProvisioning

A cc o u n t i n g a n d F i n a n c eCredit analysisCustomer servicesLoan eligibilityBanking help desk

Insurance underwriting A uditingStock & commodity tradingFinancial planningTax advisingCredit control

Aer o spa c e/ D o DLogisticsManpower planningSituation assessmentDiagnosis & repair

Inventory managementSeismic analysisTactical schedulingTrainingMunitions requirements

T ra n sp o rtat i onSchedulingPricing

Y ield managementResource allocation

B u s i n ess Ser vi c esProfit forecastingProduct selectionData dictionaryCustom interfacesCustom trainingCustom software toolsSoftware requirements

Mar k et i n g & Sa les A llocation of advertisement mediaMarket assessmentsSalespersons assignment

A ccount marketingProduct selection

Ser vi c e I n d u stryFor a list of applications in the service industrysee Motiwalla and Gargaya [1992]

Documents

Chap12. Exp Syst